Abstract
Purpose Accurate segmentation of seizure phases in intracranial EEG is essential for characterizing seizure dynamics and supporting presurgical evaluation in drug-resistant focal epilepsy. This study examines whether a semi-supervised changepoint detection framework can reliably delineate ictal onset, intra-ictal transition, and seizure termination. Methods A three-phase segmentation pipeline integrates multivariate envelope-based features, including root mean square amplitude, relative bandpower in the theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–80 Hz) bands, line length, and spectral entropy, with the Pruned Exact Linear Time algorithm. Features were extracted from sliding windows whose lengths and phase-specific weights were optimized using nested leave-one-subject-out cross-validation with Optuna. To ensure length invariance, analysis windows were randomly extended by 5–30 s before seizure onset and after seizure termination using real pre- and post-ictal data. Performance was evaluated on 179 seizure-onset-zone bipolar channels across 32 seizures from 10 patients. Results Mean absolute errors were $$4.19 \pm 2.69$$ 4.19 ± 2.69 s for seizure onset, $$6.93 \pm 5.75$$ 6.93 ± 5.75 s for intra-ictal transition, and $$3.82 \pm 4.24$$ 3.82 ± 4.24 s for seizure termination. Detection accuracies within $$\pm 5$$ ± 5 s were 71.6% for onset, 60.0% for transition, and 75.0% for termination. Phase-specific feature importance analysis revealed distinct and evolving contributions of amplitude-, spectral-, and complexity-based measures across seizure phases. Conclusion The proposed framework achieves temporal precision comparable to reported inter-rater reliability (Cohen’s $$\kappa = 0.35$$ κ = 0.35 –0.69) and provides an interpretable, data-driven approach for comprehensive seizure phase characterization, with potential utility in clinical decision-making.